Working memory span training improves working memory capacity / 中华行为医学与脑科学杂志

Objective:To explore whether working memory span training can expand working memory capacity.Methods:A randomized controlled trial design was adopted and a total of 60 healthy college students were recruited and randomly divided into training group ( n=30, receiving adaptive training of spatial breadth task) and control group ( n=30, receiving non-adaptive training of low difficulty spatial breadth task). The cognitive behavior and event-related potential (ERP) data of all subjects when completing the change awareness task were collected before and after training.The SPSS 22.0 statistical software was used for data analysis. The differences between the training group and the control group before and after training were compared by repeated measurement analysis of variance. Results:Repeated measurement ANOVA showed that there were significant time and group interactions at the levels of cognitive behavior(K score, F=5.352, P=0.025) and ERP (CDA, F=4.644, P=0.037) levels. Further post test found that compared with pre-training (pre-test), the K-score ((0.51±0.93), (1.61±1.07), F=26.81, P<0.001) and CDA ((-1.49±1.07)μV, (-2.03±0.94)μV, F=4.731, P=0.041) of the training group increased significantly after training (post-test), and there was no significant difference in K-score and CDA of the control group before and after training (boh P>0.05). Conclusion:Working memory span task can be used as an effective training paradigm to improve working memory capacity.

Análisis multitemporal de cambios de uso de la tierra en San Fernando, Tamaulipas, durante el periodo 1987 a 2017 / Multitemporal land use change analysis in San Fernando, Tamaulipas for the Period 1987 to 2017

Resumen El cambio de uso de la tierra (CUT) tiende a impactar de manera negativa los procesos atmosféricos y climáticos globales. El presente artículo tuvo como objetivo evaluar el CUT en el municipio de San Fernando, Tamaulipas, México, durante el periodo 1987 a 2017. Se utilizó el método de clasificación por segmentación de imágenes satelitales, de los años 1987, 1997, 2007 y 2017, el cual, permitió reducir el ruido característico de la clasificación basada en pixeles. Sin embargo, fue necesario editar los resultados, para recuperar los asentamientos humanos, eliminar nubes y sombras, y reducir los efectos de confusión entre cobertura vegetal y zonas agrícolas con cultivos presentes, para evitar introducir CUT artificiales en las estadísticas obtenidas. El análisis multitemporal mostró una clara tendencia en la reducción de la cobertura vegetal (-6.53 %) y del área sin vegetación aparente (-1.71 %). También se observó un importante incremento en el uso agrícola (+7.61 %), que no pareció estar asociado a un incremento en asentamientos humanos (+0.08 %). La metodología desarrollada parece ser adecuada y fácil de implementar para el análisis de CUT en regiones de interés.

Abstract Land Use Change (LUC) tends to have a negative effect on global atmospheric and climate processes. The objective of this paper was to assess the LUC for the San Fernando, Tamaulipas, Mexico municipality, during the period comprehended between 1987 and 2017. The classification by segmentation method was applied to satellite images obtained from 1987, 1997, 2007 and 2017, which allowed for a reduction in the noise that is characteristic of pixel-based classification. However, it was necessary to edit the results in order to recover human settlements, eliminate clouds and shadows, and reduce the confusion between vegetation cover and cultivated agricultural land in order to avoid introducing artificial LUC in the statistics produced. The multitemporal analysis showed a clear trend in the reduction of vegetation cover (-6.53 %) and of areas devoid of vegetation (-1.71 %). Likewise, the results also highlight a significant increase of agricultural land (+7.61 %), which seems to be unassociated with the increase of human settlements (+0.08 %). The developed methodology seems to be appropriate and of easy implementation to carry out the LUC analysis in other regions of interest.

Land cover modification geoindicator applied in a tropical coastal environment / Geoindicador de modificación de la cobertura aplicado a un ambiente costero tropical

Environmental changes due to natural processes and anthropic modifications can be characterized by the degree of land cover modification and its environmental implications over time. The main goal of the present study was to propose and apply a land cover modification geoindicator in order to assess the environmental condition of the territory per landscape units. It was designed to interpret diffuse information and transform it into a synthetic indicator that will be useful for environmental managers. The geoindicator evaluation was performed through a multi-temporal analysis of medium resolution Landsat satellite images and their unsupervised classification according to the direction of land use transitions. A change detection analysis between image pairs from 1973, 1991 and 2001 was made to detect unaffected areas and the areas in which positive or negative land cover changes could be observed. The proposed methodology was applied in the coastal palustrine area; specifically, in the marine-terrestrial ecotone of Campeche, Mexico. Geoindicator values during the 1974-1991 and 1991-2001 periods were low, 46.5% and 40.9%, respectively, due to the intrinsic limitations of coastal wetlands for productive activities. Urban and suburban transition areas showed high degrees of modification of about 39.5% and 32.1% for the first and the second period, respectively. Moderate modification, 4.9% in the first period and 5.7% in the second, was observed in isolated landscape units with recovering vegetation. The proposed geoindicator showed physiognomic and functional evidence of affectation levels from human activities, regeneration patterns and alteration of the landscape structure, modulated by the historical-economic process in the studied area. Rev. Biol. Trop. 62 (3): 1111-1128. Epub 2014 September 01.

Los cambios ambientales debidos tanto a procesos naturales como a las modificaciones antrópicas, se pueden caracterizar a través de la modificación de la cobertura y sus implicaciones ambientales en el tiempo. El objetivo del presente estudio es proponer y aplicar un geoindicador de modificación de la cobertura para evaluar la condición ambiental del territorio dentro del ámbito funcional de las unidades de paisaje. La evaluación del geoindicador se basa en el análisis multitemporal de imágenes de satélite Landsat de resolución espacial media y su clasificación no supervisada de acuerdo a la dirección tipificada de transiciones en el uso de la tierra. Incluye la detección de cambios entre pares de imágenes entre 1973, 1991 y 2001 para identificar áreas sin cambio y áreas en las que se observan cambios ambientales positivos o negativos con base en la cobertura. La metodología propuesta se aplicó en la zona costero-palustre de Campeche, México, y los valores encontrados durante los períodos 1974-1991 y 1991-2001 fueron: bajos, de 46.5% y 40.9%, respectivamente, debido a las limitaciones intrínsecas de los humedales costeros para las actividades primarias. Grados altos para las zonas de transición urbana y suburbana con 39.5% y 32.1% para el primero y segundo período, respectivamente. El grado de modificación moderado, oscila entre el 4.9% para el primer período y 5.7% para el segundo periodo, y se observó en unidades del paisaje en etapas sucesionales retrogresivas. El geoindicador propuesto muestra: evidencias fisonómicas directas y funcionales indirectas de la afectación a la cobertura original por las actividades humanas, los patrones dispersos de regeneración, la introducción de elementos alóctonos antrópicos y la artificialización del paisaje original.

Dinâmica do uso e cobertura da terra no sudeste de Roraima utilizando técnicas de detecção de mudanças / Land use and land cover dynamics in the southeastern Roraima using change detection techniques

Territory occupation and consolidation in the Amazon region have some specific characteristics related to the dynamics of land use and land cover conversions, which can be analyzed using orbital remote sensing images. The aim of this study was to evaluate change detection products generated by change vector analysis (AVM) and image subtraction techniques derived from linear spectral mixing modeling (MLME), applied to Thematic Mapper/Landsat optical images, to study land use and land cover conversions occurring in agricultural settlement areas in the southeastern region of Roraima, Brazil. We analyzed change images derived from application of AVM (magnitude, alpha and beta) and subtraction of fraction images (soil, vegetation and shade), for their ability to identify and discriminate the existing conversions. An extensive field work was used as a guide to define the classes. Exploratory analyses of class behaviors were made and two supervised algorithms for image classification - Bhattacharyya and Support Vector Machine - were tested. By grouping (clumping classes), we sought to optimize conversion identification in the classification products. The results indicated better Bhattacharyya region classifier performance of conversion discrimination. The use of MLME fractions difference images as input into the classifier resulted a very good or excellent classification quality, which was better in comparison with products using AVM images, either in isolation or in conjunction with MLME difference images.

A ocupação e consolidação do território na Amazônia apresentam diferentes características relacionadas à dinâmica das conversões de uso e cobertura da terra, que podem ser analisadas utilizando imagens orbitais de sensoriamento remoto. O objetivo do presente trabalho foi avaliar os produtos de detecção de mudanças gerados por análise de vetor de mudança (AVM) e subtração de imagens, a partir de imagens-fração derivadas das imagens ópticas TM/Landsat, para o estudo das conversões de uso e cobertura da terra presentes em área de colonização agrícola na região sudeste de Roraima. Analisaram-se as imagens de mudança provenientes da aplicação do AVM (magnitude, alfa e beta) e da subtração das imagens-fração (solo, sombra e vegetação) quanto à sua capacidade de identificar e discriminar as conversões existentes, de acordo com levantamento de campo. Foram testados dois algoritmos de classificação de imagens do tipo supervisionado, Bhattacharyya e Support Vector Machine. Foram feitos agrupamentos para otimizar a identificação das conversões nas classificações testadas. Houve melhor desempenho do classificador por regiões Bhattacharyya na discriminação das conversões. A utilização das imagens-diferença das frações como informação de entrada para o classificador apresentou qualidade de classificação muito boa ou excelente, sendo superior às classificações utilizando os produtos AVM, isoladamente ou em conjunto com as imagens-diferença.

Remote sensing and GIS based forest cover change detection study in Kalrayan hills, Tamil Nadu.

The present study focuses on the role of remote sensing and geographic information system (GIS) in assessment of changes in forest cover, between 1931 and 2001, in the Kalrayan hills, Tamil Nadu. The trend of forest cover changes over the time span of 70 years, was precisely analysed using high resolution Satellite data. The study revealed that the forest cover was 275.6, 481.7 and 266.5 sq.km in 1931, 1971 and 2001 respectively. It was noticed that forest cover has increased between 1931 and 1971, because of the implementation of various afforestation schemes by the forest department and scared grooves. It also revealed that the forest cover loss between 1971 and 2001 could be due to Shifting cultivation and illegal encroachments by villagers; and the forest cover drastically decreased on plateau areas due to human population pressure. The study analyses the forest cover change in the tropical deciduous forest region of the Eastern Ghats of India. It is envisaged that the study would prove the usefulness of Remote Sensing and GIS in forest restoration planning.

Investigation on changes in complex vegetation coverage using multi-temporal landsat data of Western Black sea region - A case study.

Previous studies have been able to successfully detect changes in gently-sloping forested areas with low-diversity and homogeneous vegetation cover, using medium-resolution satellite data such as landsat. The aim of the present study is to examine the capacity of multi-temporal landsat data to identify changes in forested areas with mixed vegetation and generally located on steep slopes or non-uniform topography. landsat thematic mapper (TM)and landsat enhanced thematic mapper plus (ETM+) data for the years 1987-2000 was used to detect changes within a 19,500 ha forested area in the Western Black sea region of Turkey. The data comply with the forest cover type maps previously created for forest management plans of the research area. The methods used to detect changes were: postclassification comparison, image differencing, image rationing and NDVI (Normalized Difference Vegetation Index) differencing methods. Following the supervised classification process, error matrices were used to evaluate the accuracy of classified images obtained. The overall accuracy has been calculated as 87.59% for 1987 image and as 91.81% for 2000 image. General kappa statistics have been calculated as 0.8543 and 0.9038 for 1987 and 2000, respectively. The changes identified via the post-classification comparison method were compared with other change detetion methods. Maximum coherence was found to be 74.95% at 4/3 band rate. The NDVI difference and 3rd band difference methods achieved the same coherence with slight variations. The results suggest that landsat satellite data accurately conveys the temporal changes which occur on steeply-sloping forested areas with a mixed structure, providing a limited amount of detail but with a high level of accuracy. Moreover, it has been decided that the post-classification comparison method can meet the needs of forestry activities better than other methods as it provides information about the direction of these changes.

Dynamic Process of Visual Change Detection in Human Brain:an ERP Study / 中国心理卫生杂志

Objective:To explore the spatio-temporal dynamics of brain mechanisms in visual change detection by 256-channel event-related potential (ERP) and low-resolution electromagnetic tomography (LORETA) analyses.Methods：ERP were recorded in 12 healthy participants during performing an S1-S2 matching task.Visual stimuli defined by color and shape.Each trial consisted of two sequentially presented stimuli (S1 and S2),where S2 was either the same as S1,different from S1 in shape only,different in color only,or different in both color and shape.Subjects matched the stimuli according to task demands：attending to color and attending to shape.Result：Change condition elicited change-related positivity (CRP) ranging 135～165 ms.The estimated source regions contributing to CRP were lingual gyrus and cuneus of occipital lobe.N200 was elicited in no change and task-irrelevant change condition ranging 235～275 ms.The source for N200 was in the right temporal fusiform gyrus,middle temporal gyrus and parahippocampal gyrus.In the time window of 240～320 ms,N270 was elicited in all change conditions.The N270 source was localizable to the anterior cingulated cortex and amygdala.Conclusion：In the early stage,CRP reflects the preattentive processing of visual changes.The LORETA result confirms that CRP is generated in the early visual areas.N200 may be related to the active ignored processing of task-irrelevant change.The source for N200 is in the right temporal fusiform gyrus,middle temporal gyrus and parahippocampal gyrus.In the late stage,N270 reflects the advanced processing of visual change in the human brain.The N270 source may be in anterior cingulate cortex (ACC) and amygdala.